[Objectives]This study was conducted to realize the rapid and nondestructive identification of blueberry producing areas and protect benefits of high-quality blueberry brands.[Methods]Five types of blueberries from di...[Objectives]This study was conducted to realize the rapid and nondestructive identification of blueberry producing areas and protect benefits of high-quality blueberry brands.[Methods]Five types of blueberries from different regions were selected as experimental subjects,and spectral analysis techniques were combined with deep learning.Firstly,standard normal variable transform(SNV)and convolutional smoothing(SG)were used to deal with scattering noise and other issues in original spectral data.Secondly,due to a large amount of redundant information and high correlation between adjacent wavelengths in the collected spectra,continuous projection algorithm(SPA)and partial least squares regression(PLS)were combined for screening of features with RMSE as the indicator,and 40 feature variables were obtained.Finally,a convolutional network model CNN-SE integrating a Squeeze and Excitation(SE)attention mechanism module was constructed and compared with convolutional neural network(CNN),support vector machine(SVM),and BP neural network.[Results]The CNN-SE model had the best effect,with the accuracy and precision of the test set reaching 95%and 94.56%,respectively,and the recall and F 1 score reaching 93.94%and 94.24%,respectively.[Conclusions]The CNN-SE convolution network model can realize rapid,nondestructive and high-throughout identification of blueberry producing areas.展开更多
Near infrared spectroscopy （NIR） is now probably the most popular process analytical technology （PAT） for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in moni...Near infrared spectroscopy （NIR） is now probably the most popular process analytical technology （PAT） for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in monitoring crystallization or precipitation processes because the existence of solids could cause distortion of the spectra. This phenomenon, seen as unfavorable previously, is however an indication that NIR spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was obtained using solutions and slurries of varied solution concentration, particle size, solid concentration and temperature. The data was used to build calibration models for prediction of the multiple properties of both phases. Predictive models were developed for this challenging application using an approach that combines genetic algorithm （GA） and support vector machine （SVM）. GA is used for wavelength selection and SVM for mode building. The new GA-SVM approach is shown to outperform other methods including GA-PLS （partial least squares） and traditional SVM. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of L-glutamic acid.展开更多
基金Supported by Natural Science Foundation of Heilongjiang Province(LH2022E099)Daqing Guidance Fund for Science and Technology Planning Project(zd-2023-63)San Heng San Zong Support Program of Heilongjiang Bayi Agricultural University(ZRCPY202216).
文摘[Objectives]This study was conducted to realize the rapid and nondestructive identification of blueberry producing areas and protect benefits of high-quality blueberry brands.[Methods]Five types of blueberries from different regions were selected as experimental subjects,and spectral analysis techniques were combined with deep learning.Firstly,standard normal variable transform(SNV)and convolutional smoothing(SG)were used to deal with scattering noise and other issues in original spectral data.Secondly,due to a large amount of redundant information and high correlation between adjacent wavelengths in the collected spectra,continuous projection algorithm(SPA)and partial least squares regression(PLS)were combined for screening of features with RMSE as the indicator,and 40 feature variables were obtained.Finally,a convolutional network model CNN-SE integrating a Squeeze and Excitation(SE)attention mechanism module was constructed and compared with convolutional neural network(CNN),support vector machine(SVM),and BP neural network.[Results]The CNN-SE model had the best effect,with the accuracy and precision of the test set reaching 95%and 94.56%,respectively,and the recall and F 1 score reaching 93.94%and 94.24%,respectively.[Conclusions]The CNN-SE convolution network model can realize rapid,nondestructive and high-throughout identification of blueberry producing areas.
基金UK Engineering and Physical Sciences Research Council for funding the research (EPSRCGrant Reference: EP/C001788/1)
文摘Near infrared spectroscopy （NIR） is now probably the most popular process analytical technology （PAT） for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in monitoring crystallization or precipitation processes because the existence of solids could cause distortion of the spectra. This phenomenon, seen as unfavorable previously, is however an indication that NIR spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was obtained using solutions and slurries of varied solution concentration, particle size, solid concentration and temperature. The data was used to build calibration models for prediction of the multiple properties of both phases. Predictive models were developed for this challenging application using an approach that combines genetic algorithm （GA） and support vector machine （SVM）. GA is used for wavelength selection and SVM for mode building. The new GA-SVM approach is shown to outperform other methods including GA-PLS （partial least squares） and traditional SVM. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of L-glutamic acid.
